import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objs as go

S&P 500 stock data DataSet

Historical stock data for all current S&P 500 companies

Source: https://www.kaggle.com/datasets/camnugent/sandp500?datasetId=1908&sortBy=voteCount

#get dataset from file.

df = pd.read_csv('all_stocks_5yr.csv')
df.head()

	date	open	high	low	close	volume	Name
0	2013-02-08	15.07	15.12	14.63	14.75	8407500	AAL
1	2013-02-11	14.89	15.01	14.26	14.46	8882000	AAL
2	2013-02-12	14.45	14.51	14.10	14.27	8126000	AAL
3	2013-02-13	14.30	14.94	14.25	14.66	10259500	AAL
4	2013-02-14	14.94	14.96	13.16	13.99	31879900	AAL

#check data have null or not

df.isna()

	date	open	high	low	close	volume	Name
0	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...
619035	False	False	False	False	False	False	False
619036	False	False	False	False	False	False	False
619037	False	False	False	False	False	False	False
619038	False	False	False	False	False	False	False
619039	False	False	False	False	False	False	False

619040 rows × 7 columns

#get data of AAPL stock only
df_aapl = df.loc[df['Name'] == 'AAPL']
df_aapl.head()

	date	open	high	low	close	volume	Name
1259	2013-02-08	67.7142	68.4014	66.8928	67.8542	158168416	AAPL
1260	2013-02-11	68.0714	69.2771	67.6071	68.5614	129029425	AAPL
1261	2013-02-12	68.5014	68.9114	66.8205	66.8428	151829363	AAPL
1262	2013-02-13	66.7442	67.6628	66.1742	66.7156	118721995	AAPL
1263	2013-02-14	66.3599	67.3771	66.2885	66.6556	88809154	AAPL

df_aapl.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1259 entries, 1259 to 2517
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   date    1259 non-null   object 
 1   open    1259 non-null   float64
 2   high    1259 non-null   float64
 3   low     1259 non-null   float64
 4   close   1259 non-null   float64
 5   volume  1259 non-null   int64  
 6   Name    1259 non-null   object 
dtypes: float64(4), int64(1), object(2)
memory usage: 78.7+ KB

#change type data of date column to datetime.
df_aapl.loc[:,'date'] = pd.to_datetime(df_aapl.loc[:,'date'],format="%Y/%m/%d")
df_aapl.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1259 entries, 1259 to 2517
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   date    1259 non-null   datetime64[ns]
 1   open    1259 non-null   float64       
 2   high    1259 non-null   float64       
 3   low     1259 non-null   float64       
 4   close   1259 non-null   float64       
 5   volume  1259 non-null   int64         
 6   Name    1259 non-null   object        
dtypes: datetime64[ns](1), float64(4), int64(1), object(1)
memory usage: 78.7+ KB

/var/folders/6b/grn5vmsd7rs9lmn4336vbb3h0000gn/T/ipykernel_29984/3505223316.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_aapl.loc[:,'date'] = pd.to_datetime(df_aapl.loc[:,'date'],format="%Y/%m/%d")

#AAPL Open and Close, High and Low, Volume Price in Historical(2013-1018)

fig, (ax1,ax2,ax3)= plt.subplots(3, 1, figsize=(8,8), constrained_layout = True)

ax1.plot(df_aapl["date"],df_aapl["open"],color="green", label="Open Price")
ax1.plot(df_aapl["date"], df_aapl["close"],color="red",label = "Close Price")
ax1.set_xlabel("Year")
ax1.set_ylabel("Stock Price")
ax1.legend()
ax1.set_title("AAPL Open and Close Price in Historical 2013-2018", loc = "right" , weight='bold')

ax2.plot(df_aapl["date"],df_aapl["high"],color="green", label="High Price")
ax2.plot(df_aapl["date"], df_aapl["low"],color="red",label = "Low Price")
ax2.set_xlabel("Year")
ax2.set_ylabel("High-Low Price of AALP")
ax2.legend()
ax2.set_title("AAPL High-Low Price in Historical 2013-2018",loc = "right" , weight='bold')

ax3.plot(df_aapl["date"],df_aapl["volume"], label="Volume")
ax3.set_xlabel("Year")
ax3.set_ylabel("Volumn of Stock")
ax3.legend()
ax3.set_title("Volume in Historical 2013-2018", loc = "right" , weight='bold')

plt.show()

#Trading Volume of AAPL in historical 2013-2018

fig = go.Figure(data=go.Ohlc(x=df_aapl['date'],open=df_aapl['open'],high=df_aapl['high'],low=df_aapl['low']
                             ,close=df_aapl['close'],increasing=dict(line=dict(color= '#58FA58'))
                             ,decreasing=dict(line=dict(color= '#FA5858'))))
fig.update_layout(
title = 'Apple Historical Price (USD$)',
yaxis_title = 'AAPL stock')

fig.show()